Data Tutorials

Looking to improve your data skills using tools like R or Python? Want to learn more about working with a specific NEON data product? NEON develops online tutorials to help you improve your research. These self-paced tutorials are designed for you to used as standalone help on a single topic or as a series to learn new techniques.

Code for all script based tutorials can be downloaded at the end of the tutorial. Original files can also be found on GitHub.


Introduction to Working with Raster Data in R

8 part series
Creation supported by NEON, Data Carpentry, SESYNC, and iPlant Collaborative
A series of data tutorials that teach you how to open, plot and perform basic calculations on raster data in R. It also covers key spatial attributes associated with raster data include extent, projection and resolution. Finally we cover dealing with missing and bad data when working with remote sensing imagery.

Basic R Skills

3 part series
This series provides tutorials and references on key skills needed to complete more complex tasks in R. It is not intended as an guide for the introduction to or initial learning of how to use R.

Primer on Raster Data in R

5 part series
This series provides a brief primer on raster spatial data in R.

Document Your Code with Jupyter Notebooks

2 part series
This series teaches you to use Jupyter Notebooks formats to document code and efficiently publish code results & outputs.

Introduction to Light Detection and Ranging (LiDAR) – Explore Point Clouds and Work with LiDAR Raster Data in R

6 part series
In this series we cover the basics of lidar data including 3 key lidar data products - the Canopy Height Model, Digital Surface Model (DSM) and the Digital Terrain Model (DTM). We explore lidar point clouds using the free, online 3d point cloud viewer. Finally, we cover working with LiDAR derived rasters in R.


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